System for intelligent switchgear and automatic control panel using artificial intelligence model

Abstract

An intelligent switchboard and automatic control panel system utilizing an artificial intelligence model includes: a data collection module that collects power data such as voltage, current, frequency, and power load from the power supply network in real time; an artificial intelligence model that analyzes the collected power data to detect abnormal conditions, predict load, and derive optimal power distribution plans; a control module that adjusts the power load according to the analysis results of the artificial intelligence model and performs switching operations of the switchboard and automatic control panel; and a user interface module that provides real-time monitoring information and control commands to the user.

Description

Technology Field

[0001] The present invention relates to an intelligent switchboard and automatic control panel system utilizing an artificial intelligence model. Background Technology

[0002] To maximize the efficiency and stability of the power supply system, the intelligence of switchboards and automatic control panels is required.

[0003] Conventional switchboards and automatic control panels rely on predefined rule-based control methods and have limitations in immediately detecting and responding to real-time power load changes or abnormal signs.

[0004] In particular, as the variability of electricity demand increases and the adoption of new and renewable energy expands, dynamic adjustment of the power grid is becoming increasingly important.

[0005] Furthermore, advanced analysis technology is required for fault prediction and predictive maintenance (PdM) of power facilities, and for this purpose, AI-based intelligent switchboards and automatic control panel systems are attracting attention.

[0006] This technology can enhance the reliability of power systems and reduce maintenance costs by utilizing AI models to predict power loads, detect abnormal patterns, and automatically execute optimal control commands.

[0007] Through this, we present an innovative solution capable of strengthening the stability of the power supply chain, maximizing energy efficiency, and providing customized power management services. The problem to be solved

[0008] The present invention provides an intelligent switchboard and automatic control panel system utilizing an artificial intelligence model. means of solving the problem

[0009] An intelligent switchboard and automatic control panel system utilizing an artificial intelligence model includes: a data collection module that collects power data such as voltage, current, frequency, and power load from the power supply network in real time; an artificial intelligence model that analyzes the collected power data to detect abnormal conditions, predict load, and derive optimal power distribution plans; a control module that adjusts the power load according to the analysis results of the artificial intelligence model and performs switching operations of the switchboard and automatic control panel; and a user interface module that provides real-time monitoring information and control commands to the user.

[0010] The above data collection module includes multiple sensors installed in switchboards and automatic control panels, and additionally collects environmental and equipment status data such as temperature, humidity, insulation resistance, total harmonic distortion (THD), and transformer status, in addition to voltage, current, frequency, and power load data. The collected data is transmitted to a central server or cloud after noise removal and outlier correction through an edge computing-based data preprocessing device, and includes an anomaly detection function that outputs a warning signal by comparing with a preset reference value in the event of sensor malfunction or data transmission errors.

[0011] The above artificial intelligence model includes one or more of a deep learning-based recurrent neural network (RNN), a long-term memory network (LSTM), and a modified transformer model to analyze power load patterns and predict future load changes, and includes an anomaly detection algorithm that detects anomaly patterns by comparing past power usage data with current real-time data, and continuously learns and updates control policies through reinforcement learning to perform optimal power distribution and switching control under specific power load conditions, and after performing load prediction, anomaly detection, and optimized control commands, visually displays the results to the administrator through a user interface module and provides an immediate alarm when an anomaly occurs.

[0012] The control module described above is connected to switching devices, circuit breakers, switches, load regulators, reactive power compensation devices, etc., within the distribution panel. It automatically adjusts individual load groups to optimize load distribution based on the analysis results of an artificial intelligence model, automatically establishes an alternative power supply path in the event of a fault to minimize power outage damage, disconnects some loads or performs load balancing using batteries during peak load hours by considering specific time zones or seasonal patterns, and, if an emergency is detected, guides the user to switch between manual and automatic modes, and takes emergency measures after administrator approval.

[0013] The above user interface module includes at least one of a web-based dashboard, a mobile application, and a field panel display, and visually provides real-time power data, load status, anomaly detection notifications, and current settings of switchboards and automatic control panels, and includes control options to allow an administrator to remotely adjust power distribution settings for a specific area, automatically generates and provides a report to the administrator predicting future power usage patterns based on load prediction results from an AI model and anomaly detection data, and, if an abnormal power anomaly is detected, provides a detailed warning message in the form of a pop-up including the status of the area, possible causes, and recommended response measures. Effects of the invention

[0014] The intelligent switchboard and automatic control panel system utilizing an artificial intelligence model according to the present invention can maximize power stability by optimizing the operation of the switchboard and automatic control panel through the use of artificial intelligence to analyze power loads in real time and detect abnormalities.

[0015] In addition, power waste can be minimized and operating costs reduced through automatic control and predictive load adjustment, and AI ensures power supply continuity by performing an immediate response in the event of an emergency.

[0016] This enables the provision of efficient and highly reliable power management solutions in various environments, such as industrial power systems, smart grids, and renewable energy integration. Brief explanation of the drawing

[0017] FIG. 1 is a configuration diagram for explaining an intelligent switchboard and automatic control panel system utilizing an artificial intelligence model according to an embodiment. FIG. 2 is a flowchart illustrating the basic operation of an intelligent switchboard and automatic control panel system utilizing an artificial intelligence model according to an embodiment. FIG. 3 is a flowchart for explaining the operation of an artificial intelligence model according to an embodiment. Specific details for implementing the invention

[0018] Hereinafter, embodiments are described in detail with reference to the attached drawings. However, various modifications may be made to the embodiments, and thus the scope of the patent application is not limited or restricted by these embodiments. It should be understood that all modifications, equivalents, and substitutions to the embodiments are included within the scope of the rights.

[0019] Specific structural or functional descriptions of the embodiments are disclosed for illustrative purposes only and may be modified and implemented in various forms. Accordingly, the embodiments are not limited to the specific disclosed forms, and the scope of this specification includes modifications, equivalents, or substitutions that fall within the technical concept.

[0020] Terms such as "first" or "second" may be used to describe various components, but these terms should be interpreted solely for the purpose of distinguishing one component from another. For example, the first component may be named the second component, and similarly, the second component may be named the first component.

[0021] When it is stated that a component is "connected" to another component, it should be understood that it may be directly connected to or joined to that other component, or that there may be other components in between.

[0022] The terms used in the embodiments are for illustrative purposes only and should not be interpreted as intended to be limiting. Singular expressions include plural expressions unless the context clearly indicates otherwise. In this specification, terms such as "comprising" or "having" are intended to indicate the existence of the features, numbers, steps, actions, components, parts, or combinations thereof described in the specification, and should be understood as not precluding the existence or addition of one or more other features, numbers, steps, actions, components, parts, or combinations thereof.

[0023] Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as generally understood by those skilled in the art to which the embodiments pertain. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an ideal or overly formal sense unless explicitly defined in this application.

[0024] In addition, when describing with reference to the attached drawings, identical components are assigned the same reference numeral regardless of drawing symbols, and redundant descriptions thereof are omitted. In describing the embodiments, if it is determined that a detailed description of related prior art could unnecessarily obscure the essence of the embodiments, such detailed description is omitted.

[0025] The advantages and features of the present invention and the methods for achieving them will become clear by referring to the embodiments described below in detail together with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below but may be implemented in various different forms. These embodiments are provided merely to ensure that the disclosure of the present invention is complete and to fully inform those skilled in the art of the scope of the invention, and the present invention is defined only by the scope of the claims.

[0026] In the embodiments of the present invention, unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as generally understood by those skilled in the art to which the present invention pertains. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an ideal or overly formal sense unless explicitly defined in the embodiments of the present invention.

[0027] The shapes, sizes, ratios, angles, numbers, etc. disclosed in the drawings for explaining embodiments of the present invention are exemplary, and therefore the present invention is not limited to the depicted details. Furthermore, in describing the present invention, if it is determined that a detailed description of related known technology may unnecessarily obscure the essence of the present invention, such detailed description is omitted. Where terms such as "includes," "has," or "is made up" are used in this specification, other parts may be added unless "only" is used. Where a component is expressed in the singular, it includes cases where it includes the plural unless specifically stated otherwise.

[0028] In interpreting the components, they are interpreted to include a margin of error even in the absence of a separate explicit statement.

[0029] In the case of describing a positional relationship, for example, when the positional relationship between two parts is described using expressions such as 'on,' 'upper,' 'lower,' or 'next to,' one or more other parts may be located between the two parts unless 'immediately' or 'directly' is used.

[0030] When elements or layers are referred to as "on" another element or layer, this includes cases where another layer or element is placed directly on top of or in between. Throughout the specification, the same reference numerals refer to the same components.

[0031] The size and thickness of each component shown in the drawings are illustrated for convenience of explanation, and the present invention is not necessarily limited to the size and thickness of the illustrated components.

[0032] The features of each of the various embodiments of the present invention may be combined or combined with one another, either partially or wholly, and as will be fully understood by those skilled in the art, various technical interlocking and operation are possible, and each embodiment may be implemented independently of one another or together in an interlocking relationship.

[0033] An intelligent switchboard and automatic control panel system utilizing artificial intelligence (AI) models has been developed to maximize the stability and efficiency of the power system.

[0034] This system implements the intelligence of switchboards by including real-time power data collection, AI-based analysis and prediction, automated power control, and user-friendly monitoring functions.

[0035] This allows for the optimization of power load, early detection of anomalies, reduced maintenance costs, and improved system reliability.

[0036] An intelligent switchboard and automatic control panel system utilizing an artificial intelligence model according to the present invention comprises: a data collection module that collects power data such as voltage, current, frequency, and power load in real time from a power supply network; an artificial intelligence model that analyzes the collected power data to detect abnormal conditions and derives load prediction and optimal power distribution plans; a control module that adjusts the power load according to the analysis results of the artificial intelligence model and performs switching operations of the switchboard and automatic control panel; and a user interface module that provides real-time monitoring information and control commands to a user.

[0037] The data collection module included in the intelligent switchboard and automatic control panel system utilizing the artificial intelligence model of the present invention can collect power data in real time using multiple sensors installed in the switchboard and automatic control panel.

[0038] At this time, the data acquisition module not only measures basic power measurement data such as voltage, current, frequency, and power load, but can also additionally collect environmental and equipment condition data such as temperature, humidity, insulation resistance, total harmonic distortion (THD), and transformer status for advanced analysis of the system.

[0039] In particular, to prevent data errors that may occur in the sensor, an edge computing-based data preprocessing device is applied to remove noise and automatically correct outliers.

[0040] This data can be transmitted to a cloud server or central control unit for analysis, and a warning signal can be generated based on a preset threshold in the event of sensor malfunction or data transmission errors.

[0041] This maintains data reliability and enables system operators to obtain accurate information in real time.

[0042] The artificial intelligence model included in the intelligent switchboard and automatic control panel system utilizing the artificial intelligence model of the present invention is a key element that performs power load pattern analysis, anomaly detection, prediction, and generation of optimized control commands. In this system, power load prediction combining past data and real-time data can be performed by applying deep learning-based recurrent neural networks (RNN), long short-term memory networks (LSTM), and modified transformer models.

[0043] The load prediction function enables optimal load adjustment by detecting peak times with high power consumption in advance, and can contribute to reducing power waste.

[0044] In addition, the AI-based anomaly detection algorithm can learn normal power load patterns and detect specific anomaly patterns (e.g., sudden voltage fluctuations, current spikes, harmonic distortion, etc.) in real time.

[0045] When such abnormal patterns are detected, the system immediately provides an alarm to the administrator and can prevent failures in advance by automatically diagnosing equipment or circuits with a high probability of causing problems.

[0046] Furthermore, this system can continuously learn and optimize control policies for switchboards and automatic control panels by applying reinforcement learning techniques.

[0047] In other words, the AI ​​model autonomously improves optimal power distribution and switching strategies based on actual power operation data, enabling the maximization of energy efficiency and the minimization of maintenance costs in the long term.

[0048] The control module included in the intelligent switchboard and automatic control panel system utilizing the artificial intelligence model of the present invention serves to automatically adjust the power load based on the analysis results of the AI ​​model and perform switching operations of the switchboard and automatic control panel. In this system, control commands of the AI ​​can be physically executed by linking with switching devices, circuit breakers, switches, load regulators, reactive power compensation devices, etc., within the switchboard.

[0049] The control module automatically adjusts individual load groups based on the optimal power distribution results derived from the AI ​​model, and can maintain overall power balance by shutting off some non-essential loads during power peak times or situations of rapid load increase.

[0050] In addition, if a power anomaly is detected in a specific area, the faulty section can be automatically isolated and an alternative power supply route established to minimize damage from power outages.

[0051] In the event of an emergency, it can suggest recommended measures to the manager based on the analysis results of the AI ​​model and support switching between manual and automatic modes, and, for example, apply a pre-configured emergency response algorithm to automatically perform critical power adjustments without manager approval.

[0052] The user interface module included in the intelligent switchboard and automatic control panel system utilizing the artificial intelligence model of the present invention is an important component that enables an operator to intuitively monitor and control the system status.

[0053] Specifically, the user interface module includes one or more of a web-based dashboard, a mobile application, and a field panel display, and can visually provide the manager with real-time power data, load status, anomaly detection alerts, and current settings of switchboards and automatic control panels.

[0054] Users can remotely adjust power distribution settings for specific zones through the interface and automatically view reports analyzing future power usage patterns based on load forecasts generated by AI models and anomaly detection data. For example, if the AI ​​predicts next week's peak power hours, the system includes a feature that provides warning messages to enable advance preparation.

[0055] In particular, by linking with a real-time anomaly detection system, if an abnormal power change is detected, a warning message is provided in the form of a pop-up on the user interface, and the status of the relevant area, possible causes, and recommended response measures can be explained in detail.

[0056] This enables managers to make faster and more accurate decisions and plays a crucial role in maintaining the stability of the power system.

[0057] In the following sections, we will describe the operation in which the data collection module, AI model, control module, and user interface module included in an intelligent switchboard and automatic control panel system utilizing an AI model interact organically to optimize power load and perform anomaly detection and automatic control. This document explains this in detail by dividing it into seven steps.

[0058] In step 1, the data collection module can perform data collection and preprocessing.

[0059] Specifically, multiple sensors installed in the distribution board and automatic control panel can collect power data such as voltage, current, frequency, and power load in real time.

[0060] At this time, the sensor can additionally collect environmental and equipment status data, such as temperature, humidity, insulation resistance, total harmonic distortion (THD), and transformer status, to accurately monitor the state of the power system.

[0061] In addition, the collected raw data can undergo noise removal and outlier correction through an edge computing-based data preprocessing device, and the corrected data can be transmitted to a cloud server or a central database via a communication network (Wi-Fi, 5G, IoT network). To this end, the intelligent switchboard and automatic control panel system utilizing an artificial intelligence model may further include a cloud server or a central database.

[0062] In addition, if the sensor operates abnormally or a data transmission error occurs, the data collection module can automatically generate a warning signal to the administrator.

[0063] In step 2, the artificial intelligence model can perform data analysis and anomaly detection.

[0064] Specifically, real-time power data transmitted by the data collection module can be received and stored on an AI analysis server (cloud or on-premises server), and an artificial intelligence model (deep learning-based RNN, LSTM, transformer, etc.) can learn power usage patterns by comparing and analyzing previous data and real-time data.

[0065] In addition, it can detect abnormal signs (e.g., sudden voltage changes, overload, frequency instability, harmonic anomalies, etc.) by comparing with learned patterns and determine whether an abnormality has been detected.

[0066] In this case, if the AI ​​model detects an abnormal pattern, it can calculate the probability of a problem occurring based on the data and generate a warning message to the manager including the risk level.

[0067] In step 3, the artificial intelligence model can perform power load prediction and optimization analysis.

[0068] Specifically, artificial intelligence models can predict future power loads by utilizing collected power data. In this process, the prediction model learns changes in power demand during specific time periods (e.g., peak hours, nighttime, etc.) and can predict future load patterns.

[0069] Furthermore, the AI ​​model can generate a load balancing plan by executing a power distribution optimization algorithm in preparation for peak load times. Additionally, after deriving an optimal power distribution scenario, it can transmit it to the control module to review its feasibility.

[0070] In step 4, the control module can generate and execute automatic control commands.

[0071] Specifically, based on the optimal power distribution and load adjustment plan derived by the artificial intelligence model, the control module can generate control commands for switching devices, circuit breakers, switches, load regulators, etc.

[0072] In this case, if load adjustment is required, the power supply to a specific load group can be regulated, and load management using a reactive power compensation device or a battery can be performed if necessary.

[0073] In addition, in the event of a failure, it can automatically set an alternative power supply path to minimize damage from power outages and support the stable operation of the system.

[0074] In addition, in emergency situations, a switch between automatic and manual modes is made based on the judgment of the AI ​​model, and an option can be provided for an administrator to manually intervene.

[0075] In step 5, the user interface module can perform real-time monitoring and feedback through the user interface.

[0076] Specifically, the user interface (UI) module can provide real-time information through web dashboards, mobile applications, and field panel displays, and can visually display load prediction results derived from artificial intelligence models, anomaly detection logs, and optimized power distribution strategies to managers.

[0077] As a result, administrators can check the power distribution status of specific zones through the dashboard and perform manual control remotely if necessary.

[0078] In addition, if an anomaly is detected, it can automatically generate a cause analysis report to support administrators in taking action.

[0079] In step 6, the control module and the user interface module can perform emergency response and automatic recovery.

[0080] Specifically, when an emergency situation is detected (e.g., overload, transformer failure, power outage, etc.), an artificial intelligence model can analyze the cause of the problem and generate a response scenario, and the control module can execute the emergency response scenario generated by the artificial intelligence model to reduce a specific load or activate emergency power supply.

[0081] In addition, the user interface module can send immediate warning messages to the administrator and suggest response measures recommended by the artificial intelligence model.

[0082] As a result, the administrator can perform recovery operations by approving the proposed response plan or manually adjusting it.

[0083] In step 7, the artificial intelligence model can perform continuous learning and optimization.

[0084] Specifically, the artificial intelligence model can continuously learn from operational data to improve the accuracy of power load prediction and update the model to derive more optimized load adjustment strategies.

[0085] At this time, the system's operational data can be analyzed to select a prediction model with high accuracy, and based on this, the existing artificial intelligence model can be retrained.

[0086] In addition, the learned data is stored on a cloud server and can be utilized to respond more quickly should similar abnormal patterns occur in the future.

[0087] Administrators monitor the performance of AI and can manually change specific control policies if necessary.

[0088] This system utilizes artificial intelligence to analyze power loads in real time and detect anomalies, thereby maximizing power stability by optimizing the operation of switchboards and automatic control panels.

[0089] In addition, power waste can be minimized and operating costs reduced through automatic control and predictive load adjustment, and AI ensures power supply continuity by performing an immediate response in the event of an emergency.

[0090] This enables the provision of efficient and highly reliable power management solutions in various environments, such as industrial power systems, smart grids, and renewable energy integration.

[0091] Although embodiments of the present invention have been described in more detail with reference to the attached drawings, the present invention is not necessarily limited to these embodiments and may be modified in various ways within the scope of the technical spirit of the present invention. Accordingly, the embodiments disclosed in the present invention are intended to explain, not limit, the technical spirit of the present invention, and the scope of the technical spirit of the present invention is not limited by these embodiments. Therefore, the embodiments described above should be understood as illustrative in all respects and not restrictive. The scope of protection of the present invention shall be interpreted by the claims below, and all technical spirits within an equivalent scope shall be interpreted as being included within the scope of rights of the present invention.

[0092] Therefore, other implementations, other embodiments, and equivalents to the claims also fall within the scope of the claims set forth below.

Claims

Claim 1 An intelligent switchboard and automatic control panel system utilizing an artificial intelligence model, comprising: a data collection module that collects power data such as voltage, current, frequency, and power load in real time from a power supply network; an artificial intelligence model that analyzes the collected power data to detect abnormal conditions and derives load prediction and optimal power distribution plans; a control module that adjusts the power load according to the analysis results of the artificial intelligence model and performs switching operations of the switchboard and automatic control panel; and a user interface module that provides real-time monitoring information and control commands to a user. Claim 2 In claim 1, the data collection module includes multiple sensors installed in switchboards and automatic control panels, and additionally collects environmental and equipment status data such as temperature, humidity, insulation resistance, total harmonic distortion (THD), and transformer status in addition to voltage, current, frequency, and power load data; the collected data is transmitted to a central server or cloud after noise removal and outlier correction through an edge computing-based data preprocessing device; it includes an anomaly detection function that outputs a warning signal by comparing with a preset reference value in the event of sensor abnormal operation or data transmission error; the artificial intelligence model includes one or more of a deep learning-based recurrent neural network (RNN), a long-short-term memory network (LSTM), and a modified transformer model to analyze power load patterns and predict future load changes, and includes an anomaly detection algorithm that detects anomaly patterns by comparing past power usage data with current real-time data; it continuously learns and updates control policies through reinforcement learning to perform optimal power distribution and switching control under specific power load conditions; and after performing load prediction, anomaly detection, and optimized control commands, the results are transmitted to the administrator through a user interface module. An intelligent switchboard and automatic control panel system utilizing an artificial intelligence model that provides visual display and immediate alarms in the event of an anomaly. Claim 3 In claim 1, the control module is connected to a switching device, circuit breaker, switch, load regulator, reactive power compensation device, etc. within the distribution panel, automatically adjusts individual load groups to optimize load distribution based on the analysis results of an artificial intelligence model, automatically sets an alternative power supply path in the event of a fault to minimize power outage damage, disconnects some loads or performs load distribution using batteries during peak load hours considering specific time periods or seasonal patterns, guides the user to switch between manual and automatic modes when an emergency situation is detected, and takes emergency measures after administrator approval, and the user interface module includes at least one of a web-based dashboard, a mobile application, and a field panel display, visually provides real-time power data, load status, anomaly detection notifications, and current settings of the distribution panel and automatic control panel, includes control options to allow an administrator to remotely adjust power distribution settings for a specific area, automatically generates a report predicting future power usage patterns based on the load prediction results of the AI ​​model and anomaly detection data and provides it to the administrator, and when an abnormal power anomaly is detected, provides a detailed warning message in the form of a pop-up including the status of the area, possible causes, and recommended response measures, an intelligent distribution panel utilizing an artificial intelligence model and Automatic control panel system.

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